ENHANCED RELIABLE COLLABORATIVE FILTERING FOR BOOK PREDICTION IN ACADEMIC LIBRARIES

Abstract

Book collection management in academic libraries needs special care with the basis of analyzing the rating provided by the readers. Additionally, a book loaned out count details help measure its popularity. Due to the wrong prediction of books the count of readers getting decreased. Even though multiple research works are carried out in this thrust research area, it still didn’t meet its objective of correct prediction of books. In this paper, enhanced reliable collaborative filtering (ERCF) is proposed to predict the user expected book based on the rating. Different readers provide different rating at different times. ERCF considers the dynamicity of rating and predicts the expected books. ERCF is evaluated against Kalman Filtering using benchmark metrics in MATLAB. Results with better results show that the proposed work has outperformed the baseline method.